Cost-effectiveness of diagnostic tests for threatened preterm labor in singleton pregnancy in France

Abstract Background Previous studies have showed that the early diagnosis of threatened preterm labor decreases neonatal morbidity and mortality, avoids maternal morbidity induced by antepartum bed rest and unnecessary treatment, and reduces costs. Although there are many diagnostic tests, none is clearly recommended by international guidelines. The aim of our study was to compare seven diagnostic methods in terms of effectiveness and cost using a decision analysis model in singleton pregnancy presenting threatened preterm labor, between 24 and 34 weeks of gestation. Methods Seven diagnostic strategies based on individual or combined use of the following tests: cervical length, cervical fibronectin test, cervical interleukin test and protein in maternal serum, were compared using a decision analysis model. Effectiveness was expressed in terms of serious adverse neonatal events avoided (neonatal morbidity and mortality) at the hospital discharge. The economic analysis was performed from the health care system perspective. Deterministic and probabilistic analyses were performed to test the robustness of the model. Results At 24–34 weeks of gestation, the association of cervical length and qualitative fibronectin was the most efficient strategy dominating all alternatives, reducing the perinatal death or severe neonatal morbidity rate up to 15% and the costs up to 31% according to the gestational age. This result was confirmed by the deterministic sensitivity analyses. The probabilistic analysis showed that the association of cervical length and qualitative fibronectin dominated cervical length

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PID https://www.doi.org/10.6084/m9.figshare.c.4134743
PID https://www.doi.org/10.6084/m9.figshare.c.4134743.v1
URL https://dx.doi.org/10.6084/M9.FIGSHARE.C.4134743
URL https://dx.doi.org/10.6084/m9.figshare.c.4134743.v1
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Author Desplanches, Thomas
Author Lejeune, Catherine
Author Cottenet, Jonathan
Author Sagot, Paul
Author Quantin, Catherine
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Collected From Datacite
Hosted By figshare
Publication Date 2018-06-15
Publisher figshare Academic Research System
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keyword FOS: Biological sciences
system:type dataset
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Source https://science-innovation-policy.openaire.eu/search/dataset?datasetId=dedup_wf_001::e93c4ce8d7487af314d5f97ad9776b06
Author jsonws_user
Last Updated 14 January 2021, 14:33 (CET)
Created 14 January 2021, 14:33 (CET)